After the successful launch of the Economics and Natural Language Processing (ECONLP) workshop at ACL 2018 in Melbourne, Australia and the follow-up event at EMNLP-IJCNLP 2019 in Hong Kong, China, the third edition of ECONLP will be run at EMNLP 2021.

ECONLP addresses the increasing relevance of NLP for regional, national and international economy, both in terms of already operational language technology products and systems, and newly emerging methodologies and techniques reflecting the requirements at the intersection of economics and NLP. The focus of the workshop will be on how NLP influences business relations and procedures, economic transactions, and the roles of human and computational actors involved in commercial activities.

Important Dates

  • August 26, 2021 Workshop Papers Due
  • September 16, 2021 Notification of acceptance
  • September 26, 2021 Camera-ready papers due
  • November 11, 2021 Workshop date

Invited Speaker

  • Gerard Hoberg

    Marshall School of Business, University of Southern California, Los Angeles, CA, USA


We invite two types of original and unpublished works: Long papers (8 pages) should describe solid results with strong experimental, empirical or theoretical/formal backing, short papers (4 pages) should describe work in progress where preliminary results have already been worked out. Accepted papers will appear in the workshop proceedings. All papers are allowed an unlimited but sensible number of references. Final camera-ready versions will be allowed an additional page of content to address reviewers’ comments. All submissions must be anonymized, in PDF format (using the EMNLP 2021 style sheets for the main conference; see and must be made through the Softconf website set up for this workshop (

Papers submitted to this workshop should address:

  • NLP-based (stock) market analytics, e.g., prediction of economic performance indicators (trend prediction, performance forecasting, etc.), by analyzing verbal statements of enterprises, businesses, companies, and associated legal or administrative actors
  • NLP-based product analytics, e.g., based on social and mass media monitoring, summarizing reviews, classifying and mining complaint messages and other (non)verbal types of customer reactions to products or services
  • NLP-based customer analytics, e.g., client profiling, tracking product/company preferences, screening customer reviews or complaints, identifying high-influentials in economy-related communication networks
  • NLP-based organization/enterprise analytics (e.g., tracing and pro-actively altering social images of organizational actors, risk prediction, fraud analysis, predictive analysis of annual business, sustainability and auditing reports)
  • NLP-based analysis of macro-economic phenomena in which national economies and the (inter)national banking system (IMF, Fed, PBoC, ECB) play an influential role
  • Market sentiments and emotions as evident from consumers’ and enterprises’ verbal behavior and their communication strategies about products and services
  • Competitive intelligence services based on NLP tooling
  • Relationship and interaction between quantitative (structured) economic data (e.g., contained sales databases and associated time series data) and qualitative (unstructured verbal) economic data (press releases, newswire streams, social media contents, etc.)
  • Information management based on the content-based organization, packaging and archiving of verbal communication streams of organizations and enterprises (emails, meeting minutes, business letters, internal reporting, etc.)
  • Credibility and trust models for business agents involved in the economic process (e.g., as traders, sellers, advertisers) extracted from text/opinion mining their communication behavior (including historic legacy data)
  • Deception or fake information recognition related to economic objects (such as products, advertisements, etc.) or economic actors (such as industries, companies, etc.), including opinion spam targeting or emanating from economic actors and processes
  • Verbally fluent software agents (chatbots for sales and marketing) as virtual actors in economic processes serving business interests, e.g., embodying models of persuasion, information biases, fair trading
  • Enterprise search engines (e-commerce, e-marketing)
  • Consumer search engines, market monitors, product/service recommender systems
  • Client-supplier interaction platforms (e.g., portals, helps desks, newsgroups) and transaction support systems based on written or spoken natural language communication
  • Multi-media and multi-modality interaction platforms, including written/spoken language channels, supporting economic processes
  • Specialized modes of information extraction and text mining in economic domains, e.g., temporal event or transaction mining
  • Information aggregation from single sources (e.g., review summaries, automatic threading)
  • Text generation in economic domains, e.g., review generation, complaint response generation
  • Ontologies and knowledge graphs for economics and adaptation of general-domain lexicons for economic NLP
  • Corpora and annotations policies (guidelines, metadata schemata, etc.) for economic NLP
  • Economy-specific text genres (business reports, sustainability reports, auditing documents, product reviews, economic newswire, business letters, etc.) and their usage for NLP (e.g., classification, filtering, etc.)
  • Dedicated software resources for economic NLP (e.g., NER taggers, sublanguage parsers, pipelines for processing verbale data from economic discourse)


PDF of the conference program


  • Udo Hahn, Friedrich-Schiller-Universität Jena, Germany (email)
  • Véronique Hoste, Ghent University, Belgium (email)
  • Amanda Stent, Bloomberg LP, New York City, NY, USA (email)

Program Committee

  • Sven Büchel — Friedrich-Schiller-Universität Jena, Jena, Germany
  • David Carmel — Amazon, Israel
  • Michael Chau — School of Business, University of Hong Kong, Hong Kong, China
  • Paulo Cortez — University of Minho, Guimarães, Portugal
  • Sanjiv Ranjan Das — Santa Clara University, Santa Clara, CA, USA
  • Brian Davis — School of Computing, Dublin City University, Ireland
  • Luciano Del Corro — Goldman Sachs, Germany
  • Lipika Dey — Tata Consultancy Services (TCS) Innovation Lab, New Delhi, India
  • Giuseppe Di Fabbrizio — VUI, Inc., Boston, MA, USA
  • Flavius Frasincar — Erasmus University, Rotterdam, Netherlands
  • Anjan Goswami — Adobe Inc.
  • Petr Hájek — University of Pardubice, Pardubice, Czech Republic
  • Yulan He — University of Warwick, UK
  • Qing Li — Southwestern University of Finance and Economics, Sichuan, Chengdu, China
  • Xiaodong Li — Hohai University, Nanjing, China
  • Pekka Malo — Aalto University, Aalto , Finland
  • Igor Mozetič — Jožef Stefan Institute, Ljubljana, Slovenia
  • Viktor Pekar — Aston University, Birmingham, UK
  • Nicolas Pröllochs — Universität Gießen, Gießen, Germany
  • Samuel Rönnqvist — University of Turku, Turku, Finland
  • Hiroki Sakaji — School of Engineering, The University of Tokyo, Tokyo, Japan
  • Kazuhiro Seki — University of Kobe, Japan
  • Sameena Shah — JPMorgan Chase, New York, NY, USA
  • Kiyoaki Shirai — Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan
  • Heiner Stuckenschmidt — Universität Mannheim, Mannheim, Germany
  • Jacopo Tagliabue — Coveo Labs, New York, NY, USA
  • Mengting Wan — Microsoft, Redmond, WA, USA
  • Frank Z. Xing — Nanyang Technological University (NTU), Singapore
  • Wlodek W. Zadrozny — University of North Carolina at Charlotte, Charlotte, NC, USA
  • Zhu (Drew) Zhang — Iowa State University, Ames, IA, USA